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<p>This paper proposes a integrated framework for optimal control of opinion dynamics in social networks, addressing three progressively challenging scenarios: Model-based stochastic control, where agent interactions follow known probability distributions, enabling analytical optimal policies; Model-free Reinforcement Learning (RL), where interaction randomness has unknown distributions but system dynamics are preserved; Data-driven RL for unknown systems, where time-varying network dynamics (with stochasticity constraints) are fully unknown, requiring purely observational learning. By designing an RL control framework grounded in convex quadratic optimization, we bridge model-based control and data-driven learning, offering new insights for social network manipulation and multi-agent coordination. Numerical simulations demonstrate the framework’s effectiveness.</p>

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Reinforcement learning-based optimal control for stochastic opinion dynamics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • Yajin Chen,
  • Hongwei Gao,
  • Vladimir V. Mazalov,
  • Yanshan Liu

摘要

This paper proposes a integrated framework for optimal control of opinion dynamics in social networks, addressing three progressively challenging scenarios: Model-based stochastic control, where agent interactions follow known probability distributions, enabling analytical optimal policies; Model-free Reinforcement Learning (RL), where interaction randomness has unknown distributions but system dynamics are preserved; Data-driven RL for unknown systems, where time-varying network dynamics (with stochasticity constraints) are fully unknown, requiring purely observational learning. By designing an RL control framework grounded in convex quadratic optimization, we bridge model-based control and data-driven learning, offering new insights for social network manipulation and multi-agent coordination. Numerical simulations demonstrate the framework’s effectiveness.